ComplexDnet: A Network-Based Strategy to Discover Critical Targets and Screen Active Compounds for Complex Diseases

The
etiology of complex diseases such as metabolic-associated steatohepatitis
(MASH) presents significant challenges for therapeutic discovery.
Here, we developed ComplexDnet, a transcriptome- and network-integrated
framework to prioritize disease-relevant targets. Applied across eight
cancer types, ComplexDnet achieved an average recall of 77.63%, outperforming
four advanced methods by 10–40%. Then, we applied ComplexDnet
in MASH and revealed retinoid-related orphan receptor γt (RORγt)
as a central regulator of MASH-associated inflammatory and fibrotic
cascades. Network-based virtual screening revealed panaxatriol (PXT)
as a potent RORγt inverse agonist (IC50 = 0.01 μM),
confirmed via X-ray crystallography (2.8 Å). PXT was further
shown to significantly attenuate fibrosis in murine models. These
findings demonstrated the utility of ComplexDnet in discovering functionally
and structurally relevant targets and accelerating drug discovery.
Finally, we integrated this pipeline into an open-source software
(https://github.com/sirpan/ComplexDnet), which would benefit the drug discovery community for complex diseases.